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Found 4 papers, 1 papers with code
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT).
Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search
Parallel speedup of MOPLS is higher than all other parallel algorithms with 16 and 64 processors.
Sensitivity Analysis for Computationally Expensive Models using Optimization and Objective-oriented Surrogate Approximations
In addition, the performance of Kriging is not as good as Gaussian RBF, especially in the case of high dimensional problems.
A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis
We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima.
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